Understanding and predicting the response of reservoir zooplankton communities and water quality to climate change

Abstract

Freshwater zooplankton communities are highly sensitive to environmental change and are critical indicators of water quality. Zooplankton are central organisms in freshwater food webs, composed of diverse taxa playing different functional roles in freshwater food webs as food sources for upper trophic level predators (e.g., fish and invertebrates) and as grazers of phytoplankton. Therefore, changes in zooplankton community density, biomass, composition, and migration behavior over time have direct implications for trophic level interactions and water quality. Climate change has altered freshwater ecosystem functioning through several mechanisms, including warming surface waters, declining dissolved oxygen concentrations, and changes in the timing and magnitude of phytoplankton blooms, each of which has implications for zooplankton communities. To better understand and predict zooplankton community responses to variable environmental conditions due to climate change, I used field, laboratory, modeling, and forecasting approaches. First, I assessed zooplankton community structure and migration across five 24-hour field sampling campaigns that spanned three years in a eutrophic, temperate reservoir. Specifically, I intensively sampled zooplankton dynamics across different sampling days, hours within a day, and reservoir sites and found that zooplankton community structure and migration was most variable among sampling days, suggesting that routine water quality monitoring programs aiming to characterize zooplankton should prioritize sampling efforts over several days to capture the greatest variability. Second, I used field data and multivariate analyses to assess patterns and drivers of zooplankton taxon density over six summers in the same reservoir. My findings suggested that zooplankton communities in years with warmer surface waters, lower precipitation, deeper Secchi depths, higher Schmidt stability, and lower epilimnetic nutrient concentrations favored rotifer dominance and lower cyclopoid densities. Third, I used a process-based ecosystem model to examine how warming air temperatures affect zooplankton biomass and community composition over an eight-year time series in the reservoir. I showed that warming temperatures promote greater rotifer biomass and lower crustacean biomass, which has implications for water quality. Finally, I forecasted reservoir water temperature from 1-35 days into the future using different observation frequencies to identify the lowest temporal frequency of data assimilation required to generate accurate forecasts. I found that weekly observations could be used to generate accurate water temperature forecasts up to a week in advance. This work highlighted that accurate forecasts may not necessarily require the most high-frequency observations, and that observation frequency is likely dependent on the variable and time horizon of interest. Generating accurate water temperature forecasts is particularly relevant for future development of zooplankton forecasts that need accurate water temperature forecasts as model driver data. Overall, my dissertation explores the dynamic relationship between freshwater zooplankton communities and water quality, highlighting the high variability in zooplankton structure, migration behavior, and environmental drivers over time. I demonstrate how zooplankton responses to climate change vary by taxon and emphasize their role in shaping freshwater food webs and ecosystem functioning, underscoring the important role of zooplankton communities in mediating water quality.